What are the key takeaways from this Practical AI episode?

Key insights include: **L0 Segmentation:** Not all non-adopters are the same — five distinct L0 categories exist: performative users, the disinterested, the too-busy, the job-fearful, and the quality-disappointed. Roughly 60% of resistant employees fall into the quality-disappointed category, meaning their pushback contains valid, actionable feedback about tool limitations that leaders should mine rather than dismiss.; **L2 Nontechnical Builder:** The highest-leverage hire per team is not the most AI-excited person but someone with deep company DNA — someone who knows exactly how work should look and feel. One L2 per team, paired with an L3 for governance and scalability, outperforms multiple L2s working together and produces tools SMEs actually trust.; **Threat Framing Slows Adoption:** Research consistently shows that issuing ultimatums — adopt AI by a deadline or face dismissal — actively reduces learning speed and quality. Mandating AI proficiency company-wide mirrors failed historical attempts to force universal CRM or project-management tool adoption, and the AI adoption curve mirrors the personal computer curve from the 1980s.

What did Mike Lewis discuss on Practical AI?

Mike Lewis, Chief AI Architect at Tier One Performance, presents a four-level AI proficiency framework (L0–L3) for large enterprises, arguing that organizations should stop forcing universal AI adoption and instead identify "nontechnical builders" — one per team — who can create measurable, durable AI solutions aligned to company-specific work. Key topics include: **L0 Segmentation:** Not all non-adopters are the same — five distinct L0 categories exist: performative users, the disinterested, the too-busy, the job-fearful, and the quality-disappointed. Roughly 60% of resistant employees fall into the quality-disappointed category, meaning their pushback contains valid, actionable feedback about tool limitations that leaders should mine rather than dismiss.; **L2 Nontechnical Builder:** The highest-leverage hire per team is not the most AI-excited person but someone with deep company DNA — someone who knows exactly how work should look and feel. One L2 per team, paired with an L3 for governance and scalability, outperforms multiple L2s working together and produces tools SMEs actually trust..

How long is this episode of Practical AI?

This episode is 56 minutes long. SignalCast provides an AI-generated summary so you can get the key insights in about 3 minutes.

Practical AI

AI Proficiency: From Users to Builders

August 25, 2026

56 min episode · 2 min read

Mike Lewis

Episode

56 min

Read time

2 min

Topics

Investing, Leadership, Artificial Intelligence

AI-Generated Summary

Published Aug 25, 2026

Key Takeaways

✓L0 Segmentation: Not all non-adopters are the same — five distinct L0 categories exist: performative users, the disinterested, the too-busy, the job-fearful, and the quality-disappointed. Roughly 60% of resistant employees fall into the quality-disappointed category, meaning their pushback contains valid, actionable feedback about tool limitations that leaders should mine rather than dismiss.
✓L2 Nontechnical Builder: The highest-leverage hire per team is not the most AI-excited person but someone with deep company DNA — someone who knows exactly how work should look and feel. One L2 per team, paired with an L3 for governance and scalability, outperforms multiple L2s working together and produces tools SMEs actually trust.
✓Threat Framing Slows Adoption: Research consistently shows that issuing ultimatums — adopt AI by a deadline or face dismissal — actively reduces learning speed and quality. Mandating AI proficiency company-wide mirrors failed historical attempts to force universal CRM or project-management tool adoption, and the AI adoption curve mirrors the personal computer curve from the 1980s.
✓Measurable ROI via L2 Builders: A top-four pharma client faced a $4 million document-conversion project requiring specialist SMEs. An L2 identified it as a Claude skill opportunity, built the solution in three hours, and enabled concurrent processing of thousands of documents. This outcome-based framing — not token usage or license counts — is how AI investment value becomes quantifiable.
✓Creative Solutioning Within Constraints: Most enterprise employees already have one to three AI tools burdened by governance restrictions. The practical strategy is persistent experimentation within those constraints — asking models for alternative approaches when blocked — rather than waiting for perfect tooling. This behavior, encouraged by leaders, produces transformative results over a two-to-five-year horizon.

What It Covers

Mike Lewis, Chief AI Architect at Tier One Performance, presents a four-level AI proficiency framework (L0–L3) for large enterprises, arguing that organizations should stop forcing universal AI adoption and instead identify "nontechnical builders" — one per team — who can create measurable, durable AI solutions aligned to company-specific work.

Key Questions Answered

•L0 Segmentation: Not all non-adopters are the same — five distinct L0 categories exist: performative users, the disinterested, the too-busy, the job-fearful, and the quality-disappointed. Roughly 60% of resistant employees fall into the quality-disappointed category, meaning their pushback contains valid, actionable feedback about tool limitations that leaders should mine rather than dismiss.
•L2 Nontechnical Builder: The highest-leverage hire per team is not the most AI-excited person but someone with deep company DNA — someone who knows exactly how work should look and feel. One L2 per team, paired with an L3 for governance and scalability, outperforms multiple L2s working together and produces tools SMEs actually trust.
•Threat Framing Slows Adoption: Research consistently shows that issuing ultimatums — adopt AI by a deadline or face dismissal — actively reduces learning speed and quality. Mandating AI proficiency company-wide mirrors failed historical attempts to force universal CRM or project-management tool adoption, and the AI adoption curve mirrors the personal computer curve from the 1980s.
•Measurable ROI via L2 Builders: A top-four pharma client faced a $4 million document-conversion project requiring specialist SMEs. An L2 identified it as a Claude skill opportunity, built the solution in three hours, and enabled concurrent processing of thousands of documents. This outcome-based framing — not token usage or license counts — is how AI investment value becomes quantifiable.
•Creative Solutioning Within Constraints: Most enterprise employees already have one to three AI tools burdened by governance restrictions. The practical strategy is persistent experimentation within those constraints — asking models for alternative approaches when blocked — rather than waiting for perfect tooling. This behavior, encouraged by leaders, produces transformative results over a two-to-five-year horizon.

Notable Moment

Lewis recounts bringing 200 pages of research citations to a bachelor party, reading through all of it while the younger attendees socialized. That reading session — spanning learning science, not just AI literature — became the foundation for his entire AI proficiency framework and the concept of the nontechnical builder.

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Episode Transcript

Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, x, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm. Now onto the show. Welcome to another episode of the Practical AI podcast. This is Daniel Whitenack. I am CEO at Prediction Guard, and I'm joined as always by my co host, Chris Benson, who is a principal AI and autonomy research engineer. How you doing, Chris? Doing good today. How's it going? It's it's going great. I'm continually impressed by just the the amazing AI work that's happening around, not only on on the coast, but in kind of the, I guess, the heartland, the the middle of the country where a lot of, actually, the large enterprises of of our country are are located. And we have a guest related to that today, but, but I do wanna remind folks, we're we're also involved and a sponsor of the Midwest AI Summit, which is coming up October 15 in Indianapolis. Really cool event, Chris. You were there. You saw what was going on. There's tables where you can sit down with actual AI practitioners and, you know, rather than just hear a bunch of talks, you can actually get feedback on what you're doing, suggestions, design, etcetera, and hear great talks. So recommend people check it out, Midwest AI Summit. You can use the code practical AI 20 for 20% off. But we have an amazing, an an amazing AI practitioner from from close by my area, more towards, Cincinnati. Mike Lewis was on a previous episode with us. You got a ton of great feedback on that episode. He's chief AI architect at tier one performance. Welcome, Mike. Thank you, Daniel. It's great to be here again. Yeah. Like I say, we got I I had multiple people comment on just the utility and and insights that they got out of our previous discussion. And just just for context, because I think it's so impressive what, what what you're doing and what you're involved with. Could you just set a little bit of context for kind of the the types of projects that you work on? Like, give an example of kinda some of the types of companies that you work with in relation to to AI initiatives? Yeah. Sure. So, tier one performance is an end to end organizational performance and transformation partner. What does that mean? We we we kinda help, some of the most of the largest organizations in the world rethink transformation. I I would add in though …

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